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Forecasting energy consumption from smart home sensor network by deep learning
Nilanjan Dey1; Simon Fong2; Wei Song3; Kyungeun Cho4
2018-08-21
Conference Name2017 International Conference on Smart Trends for Information Technology and Computer Communications
Source PublicationSmart Trends in Information Technology and Computer Communications
Volume876
Pages255-265
Conference DateAugust 18-19, 2017
Conference PlacePune, India
Abstract

Modern smart homes would be equipped with ZigBee sensors that connect home appliances via IoT network. Forecasting the future use of energy for the home appliances would be useful and practical for the home users. Since IoT sensors are designed to collect information in real-time from the home appliances, that include energy usage, indoor/outdoor temperatures and relative humidity measures, the data for harvesting insights should be abundant. Computationally a challenge is to seek for a most appropriate time-series forecasting algorithm that can produce the most accurate results. The difference between the traditional time-series forecasting algorithms and the one that involves IoT data is the ability to learn from the sheer volume of IoT data, which is known as big data nowadays. The sensor data can amount to a huge volume, and the energy drawn from an appliance, for example, air-conditioner can depend on multiple factors – the temperature/humidity of surrounding regions as well as the current weather at the time of the day. In this paper, such forecasting is tested with a range of time-series algorithms including the classical ones in comparison with deep learning which is acclaimed as a suitable prediction tool for learning over very non-linear and complex patterns.

KeywordIot Smart Home Energy Prediction Time-series Forecasting Deep Learning
DOI10.1007/978-981-13-1423-0_28
URLView the original
Language英語English
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorNilanjan Dey
Affiliation1.Department of Information Technology, Techno India College of Technology, Kolkata, India
2.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
3.Department of Digital Media Technology, North China University of Technology, Beijing, China
4.Department of Multimedia Engineering, Dongguk University, Seoul, South Korea
Recommended Citation
GB/T 7714
Nilanjan Dey,Simon Fong,Wei Song,et al. Forecasting energy consumption from smart home sensor network by deep learning[C],2018:255-265.
APA Nilanjan Dey,Simon Fong,Wei Song,&Kyungeun Cho.(2018).Forecasting energy consumption from smart home sensor network by deep learning.Smart Trends in Information Technology and Computer Communications,876,255-265.
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